Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations53000
Missing cells14000
Missing cells (%)1.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.3 MiB
Average record size in memory303.4 B

Variable types

Numeric11
Categorical3
Text1

Alerts

model_type has constant value "CPF" Constant
request_size has constant value "16KB" Constant
Acquisition_Score is highly overall correlated with Selection_MethodHigh correlation
DB_Memory is highly overall correlated with db_norm and 1 other fieldsHigh correlation
MAS_Workers is highly overall correlated with Selection_Method and 2 other fieldsHigh correlation
Parallel_Jobs is highly overall correlated with Selection_Method and 1 other fieldsHigh correlation
Selection_Method is highly overall correlated with Acquisition_Score and 7 other fieldsHigh correlation
db_norm is highly overall correlated with DB_Memory and 1 other fieldsHigh correlation
execution_time_full is highly overall correlated with MAS_Workers and 3 other fieldsHigh correlation
execution_time_none is highly overall correlated with Selection_MethodHigh correlation
execution_time_some is highly overall correlated with DB_Memory and 3 other fieldsHigh correlation
mas_norm is highly overall correlated with MAS_Workers and 2 other fieldsHigh correlation
par_norm is highly overall correlated with Parallel_Jobs and 1 other fieldsHigh correlation
Selection_Method has 1000 (1.9%) missing values Missing
Acquisition_Score has 13000 (24.5%) missing values Missing
Execution_Time has unique values Unique
db_norm has 1000 (1.9%) zeros Zeros
mas_norm has 1000 (1.9%) zeros Zeros
par_norm has 2000 (3.8%) zeros Zeros

Reproduction

Analysis started2025-05-18 15:44:55.823314
Analysis finished2025-05-18 15:45:22.643889
Duration26.82 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

DB_Memory
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1677.9588
Minimum250
Maximum2500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2025-05-18T17:45:22.758069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile659.09091
Q11068.1818
median1886.3636
Q32295.4545
95-th percentile2500
Maximum2500
Range2250
Interquartile range (IQR)1227.2727

Descriptive statistics

Standard deviation647.43406
Coefficient of variation (CV)0.38584621
Kurtosis-1.1340744
Mean1677.9588
Median Absolute Deviation (MAD)613.63636
Skewness-0.28260126
Sum88931818
Variance419170.86
MonotonicityNot monotonic
2025-05-18T17:45:22.892003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2500 10000
18.9%
1068.181818 7000
13.2%
1886.363636 7000
13.2%
2295.454545 6000
11.3%
863.6363636 5000
9.4%
2090.909091 4000
 
7.5%
1477.272727 4000
 
7.5%
1681.818182 3000
 
5.7%
1272.727273 3000
 
5.7%
659.0909091 2000
 
3.8%
Other values (2) 2000
 
3.8%
ValueCountFrequency (%)
250 1000
 
1.9%
454.5454545 1000
 
1.9%
659.0909091 2000
 
3.8%
863.6363636 5000
9.4%
1068.181818 7000
13.2%
1272.727273 3000
5.7%
1477.272727 4000
7.5%
1681.818182 3000
5.7%
1886.363636 7000
13.2%
2090.909091 4000
7.5%
ValueCountFrequency (%)
2500 10000
18.9%
2295.454545 6000
11.3%
2090.909091 4000
 
7.5%
1886.363636 7000
13.2%
1681.818182 3000
 
5.7%
1477.272727 4000
 
7.5%
1272.727273 3000
 
5.7%
1068.181818 7000
13.2%
863.6363636 5000
9.4%
659.0909091 2000
 
3.8%

MAS_Workers
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.730703
Minimum10
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2025-05-18T17:45:23.025759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile16.363636
Q122.727273
median22.727273
Q329.090909
95-th percentile67.272727
Maximum80
Range70
Interquartile range (IQR)6.3636364

Descriptive statistics

Standard deviation14.117261
Coefficient of variation (CV)0.49136497
Kurtosis4.2058846
Mean28.730703
Median Absolute Deviation (MAD)6.3636364
Skewness2.1453133
Sum1522727.3
Variance199.29706
MonotonicityNot monotonic
2025-05-18T17:45:23.154474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
22.72727273 24000
45.3%
29.09090909 14000
26.4%
16.36363636 6000
 
11.3%
54.54545455 1000
 
1.9%
67.27272727 1000
 
1.9%
60.90909091 1000
 
1.9%
10 1000
 
1.9%
48.18181818 1000
 
1.9%
80 1000
 
1.9%
73.63636364 1000
 
1.9%
Other values (2) 2000
 
3.8%
ValueCountFrequency (%)
10 1000
 
1.9%
16.36363636 6000
 
11.3%
22.72727273 24000
45.3%
29.09090909 14000
26.4%
35.45454545 1000
 
1.9%
41.81818182 1000
 
1.9%
48.18181818 1000
 
1.9%
54.54545455 1000
 
1.9%
60.90909091 1000
 
1.9%
67.27272727 1000
 
1.9%
ValueCountFrequency (%)
80 1000
 
1.9%
73.63636364 1000
 
1.9%
67.27272727 1000
 
1.9%
60.90909091 1000
 
1.9%
54.54545455 1000
 
1.9%
48.18181818 1000
 
1.9%
41.81818182 1000
 
1.9%
35.45454545 1000
 
1.9%
29.09090909 14000
26.4%
22.72727273 24000
45.3%

Parallel_Jobs
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9828473
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2025-05-18T17:45:23.282135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.7272727
Q17.8181818
median8.5454545
Q39.2727273
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)1.4545455

Descriptive statistics

Standard deviation2.108053
Coefficient of variation (CV)0.26407282
Kurtosis1.2983703
Mean7.9828473
Median Absolute Deviation (MAD)0.72727273
Skewness-1.448178
Sum423090.91
Variance4.4438876
MonotonicityNot monotonic
2025-05-18T17:45:23.415605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9.272727273 12000
22.6%
8.545454545 11000
20.8%
10 10000
18.9%
7.818181818 7000
13.2%
7.090909091 3000
 
5.7%
2 2000
 
3.8%
6.363636364 2000
 
3.8%
4.181818182 2000
 
3.8%
4.909090909 1000
 
1.9%
3.454545455 1000
 
1.9%
Other values (2) 2000
 
3.8%
ValueCountFrequency (%)
2 2000
 
3.8%
2.727272727 1000
 
1.9%
3.454545455 1000
 
1.9%
4.181818182 2000
 
3.8%
4.909090909 1000
 
1.9%
5.636363636 1000
 
1.9%
6.363636364 2000
 
3.8%
7.090909091 3000
 
5.7%
7.818181818 7000
13.2%
8.545454545 11000
20.8%
ValueCountFrequency (%)
10 10000
18.9%
9.272727273 12000
22.6%
8.545454545 11000
20.8%
7.818181818 7000
13.2%
7.090909091 3000
 
5.7%
6.363636364 2000
 
3.8%
5.636363636 1000
 
1.9%
4.909090909 1000
 
1.9%
4.181818182 2000
 
3.8%
3.454545455 1000
 
1.9%

model_type
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
CPF
53000 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters159000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCPF
2nd rowCPF
3rd rowCPF
4th rowCPF
5th rowCPF

Common Values

ValueCountFrequency (%)
CPF 53000
100.0%

Length

2025-05-18T17:45:23.573660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-18T17:45:23.671207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
cpf 53000
100.0%

Most occurring characters

ValueCountFrequency (%)
C 53000
33.3%
P 53000
33.3%
F 53000
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 159000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 53000
33.3%
P 53000
33.3%
F 53000
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 159000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 53000
33.3%
P 53000
33.3%
F 53000
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 159000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 53000
33.3%
P 53000
33.3%
F 53000
33.3%

request_size
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
16KB
53000 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters212000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row16KB
2nd row16KB
3rd row16KB
4th row16KB
5th row16KB

Common Values

ValueCountFrequency (%)
16KB 53000
100.0%

Length

2025-05-18T17:45:23.778671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-18T17:45:23.874394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
16kb 53000
100.0%

Most occurring characters

ValueCountFrequency (%)
1 53000
25.0%
6 53000
25.0%
K 53000
25.0%
B 53000
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 212000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 53000
25.0%
6 53000
25.0%
K 53000
25.0%
B 53000
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 212000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 53000
25.0%
6 53000
25.0%
K 53000
25.0%
B 53000
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 212000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 53000
25.0%
6 53000
25.0%
K 53000
25.0%
B 53000
25.0%

db_norm
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63464837
Minimum0
Maximum1
Zeros1000
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2025-05-18T17:45:23.973835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.18181818
Q10.36363636
median0.72727273
Q30.90909091
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.54545455

Descriptive statistics

Standard deviation0.28774847
Coefficient of variation (CV)0.45339826
Kurtosis-1.1340744
Mean0.63464837
Median Absolute Deviation (MAD)0.27272727
Skewness-0.28260126
Sum33636.364
Variance0.082799181
MonotonicityNot monotonic
2025-05-18T17:45:24.115750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 10000
18.9%
0.3636363636 7000
13.2%
0.7272727273 7000
13.2%
0.9090909091 6000
11.3%
0.2727272727 5000
9.4%
0.8181818182 4000
 
7.5%
0.5454545455 4000
 
7.5%
0.6363636364 3000
 
5.7%
0.4545454545 3000
 
5.7%
0.1818181818 2000
 
3.8%
Other values (2) 2000
 
3.8%
ValueCountFrequency (%)
0 1000
 
1.9%
0.09090909091 1000
 
1.9%
0.1818181818 2000
 
3.8%
0.2727272727 5000
9.4%
0.3636363636 7000
13.2%
0.4545454545 3000
5.7%
0.5454545455 4000
7.5%
0.6363636364 3000
5.7%
0.7272727273 7000
13.2%
0.8181818182 4000
7.5%
ValueCountFrequency (%)
1 10000
18.9%
0.9090909091 6000
11.3%
0.8181818182 4000
 
7.5%
0.7272727273 7000
13.2%
0.6363636364 3000
 
5.7%
0.5454545455 4000
 
7.5%
0.4545454545 3000
 
5.7%
0.3636363636 7000
13.2%
0.2727272727 5000
9.4%
0.1818181818 2000
 
3.8%

mas_norm
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26758148
Minimum0
Maximum1
Zeros1000
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2025-05-18T17:45:24.268844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.090909091
Q10.18181818
median0.18181818
Q30.27272727
95-th percentile0.81818182
Maximum1
Range1
Interquartile range (IQR)0.090909091

Descriptive statistics

Standard deviation0.20167516
Coefficient of variation (CV)0.75369626
Kurtosis4.2058846
Mean0.26758148
Median Absolute Deviation (MAD)0.090909091
Skewness2.1453133
Sum14181.818
Variance0.040672869
MonotonicityNot monotonic
2025-05-18T17:45:24.410104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.1818181818 24000
45.3%
0.2727272727 14000
26.4%
0.09090909091 6000
 
11.3%
0.6363636364 1000
 
1.9%
0.8181818182 1000
 
1.9%
0.7272727273 1000
 
1.9%
0 1000
 
1.9%
0.5454545455 1000
 
1.9%
1 1000
 
1.9%
0.9090909091 1000
 
1.9%
Other values (2) 2000
 
3.8%
ValueCountFrequency (%)
0 1000
 
1.9%
0.09090909091 6000
 
11.3%
0.1818181818 24000
45.3%
0.2727272727 14000
26.4%
0.3636363636 1000
 
1.9%
0.4545454545 1000
 
1.9%
0.5454545455 1000
 
1.9%
0.6363636364 1000
 
1.9%
0.7272727273 1000
 
1.9%
0.8181818182 1000
 
1.9%
ValueCountFrequency (%)
1 1000
 
1.9%
0.9090909091 1000
 
1.9%
0.8181818182 1000
 
1.9%
0.7272727273 1000
 
1.9%
0.6363636364 1000
 
1.9%
0.5454545455 1000
 
1.9%
0.4545454545 1000
 
1.9%
0.3636363636 1000
 
1.9%
0.2727272727 14000
26.4%
0.1818181818 24000
45.3%

par_norm
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74785592
Minimum0
Maximum1
Zeros2000
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2025-05-18T17:45:24.553684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.090909091
Q10.72727273
median0.81818182
Q30.90909091
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.18181818

Descriptive statistics

Standard deviation0.26350663
Coefficient of variation (CV)0.35234946
Kurtosis1.2983703
Mean0.74785592
Median Absolute Deviation (MAD)0.090909091
Skewness-1.448178
Sum39636.364
Variance0.069435743
MonotonicityNot monotonic
2025-05-18T17:45:24.692628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.9090909091 12000
22.6%
0.8181818182 11000
20.8%
1 10000
18.9%
0.7272727273 7000
13.2%
0.6363636364 3000
 
5.7%
0 2000
 
3.8%
0.5454545455 2000
 
3.8%
0.2727272727 2000
 
3.8%
0.3636363636 1000
 
1.9%
0.1818181818 1000
 
1.9%
Other values (2) 2000
 
3.8%
ValueCountFrequency (%)
0 2000
 
3.8%
0.09090909091 1000
 
1.9%
0.1818181818 1000
 
1.9%
0.2727272727 2000
 
3.8%
0.3636363636 1000
 
1.9%
0.4545454545 1000
 
1.9%
0.5454545455 2000
 
3.8%
0.6363636364 3000
 
5.7%
0.7272727273 7000
13.2%
0.8181818182 11000
20.8%
ValueCountFrequency (%)
1 10000
18.9%
0.9090909091 12000
22.6%
0.8181818182 11000
20.8%
0.7272727273 7000
13.2%
0.6363636364 3000
 
5.7%
0.5454545455 2000
 
3.8%
0.4545454545 1000
 
1.9%
0.3636363636 1000
 
1.9%
0.2727272727 2000
 
3.8%
0.1818181818 1000
 
1.9%

execution_time_none
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.679925
Minimum33.168017
Maximum61.122635
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2025-05-18T17:45:24.867871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum33.168017
5-th percentile38.63416
Q146.114866
median50.182076
Q354.171943
95-th percentile59.485618
Maximum61.122635
Range27.954617
Interquartile range (IQR)8.0570768

Descriptive statistics

Standard deviation5.9519331
Coefficient of variation (CV)0.1198056
Kurtosis0.065865106
Mean49.679925
Median Absolute Deviation (MAD)4.06721
Skewness-0.43871084
Sum2633036
Variance35.425507
MonotonicityNot monotonic
2025-05-18T17:45:25.063917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.98368018 1000
 
1.9%
47.41058359 1000
 
1.9%
47.90340812 1000
 
1.9%
50.91144404 1000
 
1.9%
50.89651043 1000
 
1.9%
56.14801495 1000
 
1.9%
51.66680864 1000
 
1.9%
46.67794791 1000
 
1.9%
43.41506408 1000
 
1.9%
46.6238781 1000
 
1.9%
Other values (43) 43000
81.1%
ValueCountFrequency (%)
33.16801734 1000
1.9%
36.66727047 1000
1.9%
38.63415974 1000
1.9%
39.38674531 1000
1.9%
41.89242789 1000
1.9%
41.91707873 1000
1.9%
42.49697436 1000
1.9%
43.17978545 1000
1.9%
43.41506408 1000
1.9%
44.74180316 1000
1.9%
ValueCountFrequency (%)
61.12263475 1000
1.9%
61.11460639 1000
1.9%
59.48561802 1000
1.9%
57.67135775 1000
1.9%
56.99149622 1000
1.9%
56.14801495 1000
1.9%
55.47728265 1000
1.9%
55.28856306 1000
1.9%
55.13553937 1000
1.9%
55.01954731 1000
1.9%

execution_time_some
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.057113
Minimum53.307408
Maximum74.189446
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2025-05-18T17:45:25.249214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum53.307408
5-th percentile57.205611
Q163.906483
median66.188877
Q369.446698
95-th percentile72.411712
Maximum74.189446
Range20.882038
Interquartile range (IQR)5.5402145

Descriptive statistics

Standard deviation4.3719085
Coefficient of variation (CV)0.066183766
Kurtosis0.44698567
Mean66.057113
Median Absolute Deviation (MAD)2.9440325
Skewness-0.71366667
Sum3501027
Variance19.113584
MonotonicityNot monotonic
2025-05-18T17:45:25.452156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56.96387331 1000
 
1.9%
69.44669783 1000
 
1.9%
66.16816433 1000
 
1.9%
66.12527456 1000
 
1.9%
72.1656496 1000
 
1.9%
68.71532615 1000
 
1.9%
68.47076344 1000
 
1.9%
68.15332205 1000
 
1.9%
69.60724775 1000
 
1.9%
74.1894461 1000
 
1.9%
Other values (43) 43000
81.1%
ValueCountFrequency (%)
53.30740781 1000
1.9%
56.96387331 1000
1.9%
57.20561128 1000
1.9%
57.22747625 1000
1.9%
57.82564027 1000
1.9%
60.10844761 1000
1.9%
60.95935758 1000
1.9%
61.76857184 1000
1.9%
62.73609663 1000
1.9%
62.77563811 1000
1.9%
ValueCountFrequency (%)
74.1894461 1000
1.9%
74.03308638 1000
1.9%
72.41171207 1000
1.9%
72.1656496 1000
1.9%
70.31171295 1000
1.9%
70.26834586 1000
1.9%
70.11440259 1000
1.9%
70.05269296 1000
1.9%
69.87869999 1000
1.9%
69.83344088 1000
1.9%

execution_time_full
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.04322
Minimum78.530944
Maximum121.48135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2025-05-18T17:45:25.671244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum78.530944
5-th percentile87.48983
Q1102.88044
median106.71147
Q3112.07038
95-th percentile118.06998
Maximum121.48135
Range42.950402
Interquartile range (IQR)9.1899396

Descriptive statistics

Standard deviation8.6335
Coefficient of variation (CV)0.081414914
Kurtosis0.94676894
Mean106.04322
Median Absolute Deviation (MAD)3.9852387
Skewness-0.90400609
Sum5620290.9
Variance74.537323
MonotonicityNot monotonic
2025-05-18T17:45:25.883081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98.37560471 1000
 
1.9%
107.2664367 1000
 
1.9%
106.112248 1000
 
1.9%
110.6032316 1000
 
1.9%
109.7838115 1000
 
1.9%
114.5446188 1000
 
1.9%
110.5207378 1000
 
1.9%
107.0857247 1000
 
1.9%
106.148368 1000
 
1.9%
112.4654526 1000
 
1.9%
Other values (43) 43000
81.1%
ValueCountFrequency (%)
78.53094426 1000
1.9%
85.86327372 1000
1.9%
87.48983029 1000
1.9%
91.53660075 1000
1.9%
93.4593245 1000
1.9%
94.7243686 1000
1.9%
95.15727079 1000
1.9%
95.73350033 1000
1.9%
95.81895138 1000
1.9%
98.37560471 1000
1.9%
ValueCountFrequency (%)
121.4813467 1000
1.9%
118.9167934 1000
1.9%
118.0699811 1000
1.9%
118.0313663 1000
1.9%
117.0739407 1000
1.9%
115.6852811 1000
1.9%
114.9736786 1000
1.9%
114.609915 1000
1.9%
114.5446188 1000
1.9%
113.23575 1000
1.9%
Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
2025-05-18T17:45:26.433429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.566038
Min length10

Characters and Unicode

Total characters560000
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConfig_665
2nd rowConfig_665
3rd rowConfig_665
4th rowConfig_665
5th rowConfig_665
ValueCountFrequency (%)
config_665 1000
 
1.9%
config_480 1000
 
1.9%
config_111 1000
 
1.9%
config_1609 1000
 
1.9%
config_1545 1000
 
1.9%
config_307 1000
 
1.9%
config_1014 1000
 
1.9%
config_227 1000
 
1.9%
config_862 1000
 
1.9%
config_1418 1000
 
1.9%
Other values (43) 43000
81.1%
2025-05-18T17:45:27.128045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 53000
9.5%
n 53000
9.5%
f 53000
9.5%
i 53000
9.5%
g 53000
9.5%
_ 53000
9.5%
o 53000
9.5%
1 51000
9.1%
6 24000
 
4.3%
8 19000
 
3.4%
Other values (7) 95000
17.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 560000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 53000
9.5%
n 53000
9.5%
f 53000
9.5%
i 53000
9.5%
g 53000
9.5%
_ 53000
9.5%
o 53000
9.5%
1 51000
9.1%
6 24000
 
4.3%
8 19000
 
3.4%
Other values (7) 95000
17.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 560000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 53000
9.5%
n 53000
9.5%
f 53000
9.5%
i 53000
9.5%
g 53000
9.5%
_ 53000
9.5%
o 53000
9.5%
1 51000
9.1%
6 24000
 
4.3%
8 19000
 
3.4%
Other values (7) 95000
17.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 560000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 53000
9.5%
n 53000
9.5%
f 53000
9.5%
i 53000
9.5%
g 53000
9.5%
_ 53000
9.5%
o 53000
9.5%
1 51000
9.1%
6 24000
 
4.3%
8 19000
 
3.4%
Other values (7) 95000
17.0%

Selection_Method
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing1000
Missing (%)1.9%
Memory size2.6 MiB
BO
40000 
LHS
12000 

Length

Max length3
Median length2
Mean length2.2307692
Min length2

Characters and Unicode

Total characters116000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLHS
2nd rowLHS
3rd rowLHS
4th rowLHS
5th rowLHS

Common Values

ValueCountFrequency (%)
BO 40000
75.5%
LHS 12000
 
22.6%
(Missing) 1000
 
1.9%

Length

2025-05-18T17:45:27.249471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-18T17:45:27.328641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
bo 40000
76.9%
lhs 12000
 
23.1%

Most occurring characters

ValueCountFrequency (%)
B 40000
34.5%
O 40000
34.5%
L 12000
 
10.3%
H 12000
 
10.3%
S 12000
 
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 116000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 40000
34.5%
O 40000
34.5%
L 12000
 
10.3%
H 12000
 
10.3%
S 12000
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 116000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 40000
34.5%
O 40000
34.5%
L 12000
 
10.3%
H 12000
 
10.3%
S 12000
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 116000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 40000
34.5%
O 40000
34.5%
L 12000
 
10.3%
H 12000
 
10.3%
S 12000
 
10.3%

Execution_Time
Real number (ℝ)

Unique 

Distinct53000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.000537
Minimum41.009472
Maximum59.678547
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2025-05-18T17:45:27.463176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum41.009472
5-th percentile46.684926
Q148.644981
median50.012669
Q351.36216
95-th percentile53.274442
Maximum59.678547
Range18.669075
Interquartile range (IQR)2.7171786

Descriptive statistics

Standard deviation2.0027497
Coefficient of variation (CV)0.040054563
Kurtosis-0.032472764
Mean50.000537
Median Absolute Deviation (MAD)1.3577838
Skewness-0.015221072
Sum2650028.4
Variance4.0110062
MonotonicityNot monotonic
2025-05-18T17:45:27.631349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.358887 1
 
< 0.1%
51.20883021 1
 
< 0.1%
49.69848092 1
 
< 0.1%
47.15453926 1
 
< 0.1%
50.56748431 1
 
< 0.1%
49.12829953 1
 
< 0.1%
47.79294518 1
 
< 0.1%
50.72869087 1
 
< 0.1%
51.3577557 1
 
< 0.1%
52.69263007 1
 
< 0.1%
Other values (52990) 52990
> 99.9%
ValueCountFrequency (%)
41.00947232 1
< 0.1%
41.04953831 1
< 0.1%
41.9957318 1
< 0.1%
42.21761179 1
< 0.1%
42.34032172 1
< 0.1%
42.52650443 1
< 0.1%
42.71417874 1
< 0.1%
42.71544762 1
< 0.1%
42.86981271 1
< 0.1%
42.87903838 1
< 0.1%
ValueCountFrequency (%)
59.67854692 1
< 0.1%
57.94651045 1
< 0.1%
57.42457457 1
< 0.1%
57.27716406 1
< 0.1%
57.26909538 1
< 0.1%
57.23851212 1
< 0.1%
57.19223193 1
< 0.1%
57.10960326 1
< 0.1%
57.05812289 1
< 0.1%
57.05142592 1
< 0.1%

Acquisition_Score
Real number (ℝ)

High correlation  Missing 

Distinct40
Distinct (%)0.1%
Missing13000
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean32.065296
Minimum29.426147
Maximum33.667074
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2025-05-18T17:45:27.794420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum29.426147
5-th percentile30.715467
Q131.490229
median32.116818
Q332.796075
95-th percentile33.350153
Maximum33.667074
Range4.2409267
Interquartile range (IQR)1.3058453

Descriptive statistics

Standard deviation0.92884823
Coefficient of variation (CV)0.028967399
Kurtosis-0.09046461
Mean32.065296
Median Absolute Deviation (MAD)0.69219252
Skewness-0.44373357
Sum1282611.9
Variance0.86275903
MonotonicityNot monotonic
2025-05-18T17:45:27.947969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
31.07690816 1000
 
1.9%
30.7239509 1000
 
1.9%
31.86149526 1000
 
1.9%
31.73394251 1000
 
1.9%
31.61185659 1000
 
1.9%
31.86268466 1000
 
1.9%
31.78166736 1000
 
1.9%
31.39711432 1000
 
1.9%
32.66601177 1000
 
1.9%
32.30204138 1000
 
1.9%
Other values (30) 30000
56.6%
(Missing) 13000
24.5%
ValueCountFrequency (%)
29.42614727 1000
1.9%
30.55427732 1000
1.9%
30.7239509 1000
1.9%
30.75817098 1000
1.9%
31.03715998 1000
1.9%
31.07690816 1000
1.9%
31.08399061 1000
1.9%
31.09728684 1000
1.9%
31.14346619 1000
1.9%
31.39711432 1000
1.9%
ValueCountFrequency (%)
33.66707395 1000
1.9%
33.42629867 1000
1.9%
33.34614489 1000
1.9%
33.24793402 1000
1.9%
33.23296829 1000
1.9%
33.19349736 1000
1.9%
33.12837603 1000
1.9%
32.99391023 1000
1.9%
32.87655964 1000
1.9%
32.83980092 1000
1.9%

Interactions

2025-05-18T17:45:20.393625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:44:58.416853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:00.894988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:04.342585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:06.912080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:09.513545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:11.506193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:13.146070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:14.623131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:16.194649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:17.910596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:20.522520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:44:58.618678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:01.172471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:04.555771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:07.207585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:09.758807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:11.654161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:13.277883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:14.748936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:16.331172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:18.943659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:20.648939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:44:58.887894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:01.419691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:04.811339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:07.545724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:10.086198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:11.828659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:13.420054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:14.881886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:16.473235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:19.095966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:20.786791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:44:59.087767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:01.640884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:05.051125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:07.768802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:10.282882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:11.988967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:13.560787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:15.017763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:16.614301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:19.258136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:20.926656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:44:59.314213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:01.858638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:05.298136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:07.970822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:10.457483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:12.136667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:13.695493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:15.143930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:16.750593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:19.409341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:21.058141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:44:59.518692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:02.075017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:05.512619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:08.164015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:10.594066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:12.283832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:13.830781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:15.320003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:16.950007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:19.554765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:21.196488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:44:59.705180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:02.291354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:05.718647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:08.388300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:10.740652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:12.404446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:13.966059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:15.474520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:17.109697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:19.691127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:21.315505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:44:59.940217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:02.519083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:05.938680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:08.608942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:10.905845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:12.533407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:14.101310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:15.625518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:17.300018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:19.829996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:21.443191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:00.192339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:02.753251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:06.192961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:08.809555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:11.047505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:12.668725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:14.233376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:15.775272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:17.484311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:19.966708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:21.582165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:00.385665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:03.853403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:06.439823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:09.045815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:11.194391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:12.812292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:14.357527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:15.914279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:17.625803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:20.105588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:21.719445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:00.633475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:04.144886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:06.689584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:09.285555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:11.358352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:13.008598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:14.495583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:16.058122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:17.781822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T17:45:20.262779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-18T17:45:28.083297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Acquisition_ScoreDB_MemoryExecution_TimeMAS_WorkersParallel_JobsSelection_Methoddb_normexecution_time_fullexecution_time_noneexecution_time_somemas_normpar_norm
Acquisition_Score1.0000.0560.0000.2430.2031.0000.0560.0060.0600.0990.2430.203
DB_Memory0.0561.000-0.002-0.029-0.0900.4811.0000.484-0.0540.725-0.029-0.090
Execution_Time0.000-0.0021.0000.0020.0020.000-0.002-0.0020.002-0.0060.0020.002
MAS_Workers0.243-0.0290.0021.000-0.2850.801-0.029-0.5530.154-0.4241.000-0.285
Parallel_Jobs0.203-0.0900.002-0.2851.0000.643-0.0900.495-0.1080.357-0.2851.000
Selection_Method1.0000.4810.0000.8010.6431.0000.4810.6180.5630.7540.8010.643
db_norm0.0561.000-0.002-0.029-0.0900.4811.0000.484-0.0540.725-0.029-0.090
execution_time_full0.0060.484-0.002-0.5530.4950.6180.4841.000-0.1470.725-0.5530.495
execution_time_none0.060-0.0540.0020.154-0.1080.563-0.054-0.1471.000-0.1390.154-0.108
execution_time_some0.0990.725-0.006-0.4240.3570.7540.7250.725-0.1391.000-0.4240.357
mas_norm0.243-0.0290.0021.000-0.2850.801-0.029-0.5530.154-0.4241.000-0.285
par_norm0.203-0.0900.002-0.2851.0000.643-0.0900.495-0.1080.357-0.2851.000

Missing values

2025-05-18T17:45:21.925583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-18T17:45:22.190546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-18T17:45:22.515224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DB_MemoryMAS_WorkersParallel_Jobsmodel_typerequest_sizedb_normmas_normpar_normexecution_time_noneexecution_time_someexecution_time_fullConfigurationSelection_MethodExecution_TimeAcquisition_Score
01068.18181854.5454554.909091CPF16KB0.3636360.6363640.36363653.9836856.96387398.375605Config_665LHS47.358887NaN
11068.18181854.5454554.909091CPF16KB0.3636360.6363640.36363653.9836856.96387398.375605Config_665LHS48.502824NaN
21068.18181854.5454554.909091CPF16KB0.3636360.6363640.36363653.9836856.96387398.375605Config_665LHS49.153516NaN
31068.18181854.5454554.909091CPF16KB0.3636360.6363640.36363653.9836856.96387398.375605Config_665LHS50.420709NaN
41068.18181854.5454554.909091CPF16KB0.3636360.6363640.36363653.9836856.96387398.375605Config_665LHS43.697916NaN
51068.18181854.5454554.909091CPF16KB0.3636360.6363640.36363653.9836856.96387398.375605Config_665LHS49.335430NaN
61068.18181854.5454554.909091CPF16KB0.3636360.6363640.36363653.9836856.96387398.375605Config_665LHS48.741557NaN
71068.18181854.5454554.909091CPF16KB0.3636360.6363640.36363653.9836856.96387398.375605Config_665LHS50.388335NaN
81068.18181854.5454554.909091CPF16KB0.3636360.6363640.36363653.9836856.96387398.375605Config_665LHS49.278004NaN
91068.18181854.5454554.909091CPF16KB0.3636360.6363640.36363653.9836856.96387398.375605Config_665LHS49.440255NaN
DB_MemoryMAS_WorkersParallel_Jobsmodel_typerequest_sizedb_normmas_normpar_normexecution_time_noneexecution_time_someexecution_time_fullConfigurationSelection_MethodExecution_TimeAcquisition_Score
52990659.09090929.0909094.181818CPF16KB0.1818180.2727270.27272748.95317757.82564105.820213Config_328NaN50.531538NaN
52991659.09090929.0909094.181818CPF16KB0.1818180.2727270.27272748.95317757.82564105.820213Config_328NaN51.020631NaN
52992659.09090929.0909094.181818CPF16KB0.1818180.2727270.27272748.95317757.82564105.820213Config_328NaN50.507116NaN
52993659.09090929.0909094.181818CPF16KB0.1818180.2727270.27272748.95317757.82564105.820213Config_328NaN49.800054NaN
52994659.09090929.0909094.181818CPF16KB0.1818180.2727270.27272748.95317757.82564105.820213Config_328NaN47.896358NaN
52995659.09090929.0909094.181818CPF16KB0.1818180.2727270.27272748.95317757.82564105.820213Config_328NaN51.525545NaN
52996659.09090929.0909094.181818CPF16KB0.1818180.2727270.27272748.95317757.82564105.820213Config_328NaN48.176005NaN
52997659.09090929.0909094.181818CPF16KB0.1818180.2727270.27272748.95317757.82564105.820213Config_328NaN48.717757NaN
52998659.09090929.0909094.181818CPF16KB0.1818180.2727270.27272748.95317757.82564105.820213Config_328NaN51.554553NaN
52999659.09090929.0909094.181818CPF16KB0.1818180.2727270.27272748.95317757.82564105.820213Config_328NaN50.747362NaN